Automated intelligent online healthcare ontology Integration

Abstract

Knowledge graphs have emerged as a powerful dynamic knowledge representation model for predicting hidden patterns and relationships in medical and healthcare domains for medical diagnosis and disease prediction. However, generating, constructing, and integrating knowledge graphs for this domain is still challenging research area for such heterogeneous domain. In this paper, a framework for automatic disease knowledge graph (KG) construction and intelligent ontology integration with standard human disease ontology (DO) is developed. A major component of this framework is developing an enhanced diseases' knowledge graph that is based on collecting medical facts from medical platforms and social networks, including symptoms, causes, risk factors and prevention factors. This knowledge graph represents a major base for intelligent diagnosis and disease prediction systems. The developed disease knowledge graph includes diseases' symptoms, causes, risk factors and prevention factors and integrated with DO by more than 400 diseases. The knowledge graph presented is a step not only towards building an enriched knowledge graph for professional staff and normal users. The graph is also a step towards integrating two standard ontologies human disease and symptom ontologies that are not linked or integrated till now.